EBM is a commitment to using best evidence to inform therapeutic decisions
Evidence based medicine is the conscientious, explicit, and judicious use of current best evidence in making decisions about the care of individual patients. The practice of evidence based medicine means integrating individual clinical expertise with the best available external clinical evidence from systematic research.
Sackett et al. (1996)
EBM is the use of mathematical estimates of the chance of benefit and the risk of harm, derived from high-quality research on population samples, to inform clinical decision-making
Greenhalgh (2012)
GRADE: Grading of Recommendations, Assessment, Development and Evaluation
GRADE offers a system for rating quality of evidence in systematic reviews and guidelines and grading strength of evidence of recommendations in guidelines
Guyatt et al. (2011) p. 384
Factors in considering strength of recommendation
Guyatt et al. (2008)
| PICO | CAST |
|---|---|
| Participants | Patients 6 days to 2 years following myocardial infarction with ventricular premature depolarizations who responded to antiarrythmic agent. |
| Intervention | Flecainide, encainide, moricizine |
| Comparator | Placebo |
| Outcome | Death or cardiac arrest with resuscitation due to arrhythmia |
| PICO | WHI |
|---|---|
| Participants | Women 50–79 years who were postmenopausal. |
| Intervention | Oestrogen plus progesterone (for women with an intact uterus) or oestrogen alone |
| Comparator | Placebo |
| Outcome | Coronary heart disease (acute myocardial infarction, death due to coronary heart disease, or silent myocardial infarction) |
How do we explain the very different results of the observational studies compared to WHI?
Necessary conditions for a factor to be a confounding factor between exposure and outcome:
| Statistically significant result | Non-statistically significant result | |
|---|---|---|
| Adequately powered test | Reject the null. Accept the alternative hypothesis | The test failed to reject the null. Either the null is true or the effect size is smaller than was tested |
| Underpowered test | Provisionally accept the alternative hypothesis | Underdetermined result. The test is unable to detect effect sizes that might be important. |
Precise, but confusing
If the study was repeated many times, and the same procedure was used to calculate the 95% confidence interval, in the long run, you would expect the calculated 95% confidence intervals would include the true value of the parameter 95% of the time
A 95% confidence interval provides the range of values that are not statistically different from the observed point estimate at the 0.05 level
Less precise, but useful
The confidence interval provides a range of plausible values for the unknown parameter
The lower limit is a likely lower bound estimate of the parameter; the upper limit a likely upper bound
Incorrect and misleading
You can be 95% confident that the true value lies between the observed confidence interval
The 95% confidence interval has a 95% chance of including the true effect size
What are your expectations when you toss a fair coin 10 times?
## [1] 0 1 1 1 1 0 0 1 0 0
## [1] 1 0 0 0 0 1 0 0 1 0
## [1] 0 1 1 1 0 0 0 0 0 0
In these series of experiments we saw 5, 3 and 3 \(H\).
## [1] 5 8 5 5 7 5 7 4 7 4
Imagine you have coin of unknown bias (i.e. the probability of \(H\) is unknown—it is unknown whether the coin is fair, favours \(T\) or favours \(H\)). What test could you conduct to assess whether the coin is fair?
Attempt to describe the hypothesis you are testing and the statistical model you are using for the test.
The mean number of \(H\) for the fair coin was 4.998 and for the coin of unknown bias was 6.9982.
CAST Investigators. 1991. “Mortality and morbidity in patients receiving ecainide, flecainide or placebo.” New England Journal of Medicine 324 (12): 781–88.
Greenhalgh, T. 2012. “Why do we always end up here? Evidence-based medicine’s conceptual cul-de-sacs and some off-road alternative routes.” Journal of Primary Health Care 4 (2): 92–97. https://www.rnzcgp.org.nz/assets/documents/Publications/JPHC/June-2012/JPHC-Vol-4-No-2-June-2012.pdf{\#}page=5.
Guyatt, G H, A D Oxman, R Kunz, Y Falck-Ytter, G E Vist, A Liberati, and H J Schunemann. 2008. “Going from evidence to recommendations.” BMJ 336 (7652): 1049–51. https://doi.org/10.1136/bmj.39493.646875.AE.
Guyatt, Gordon, Andrew D Oxman, Elie A Akl, Regina Kunz, Gunn Vist, Jan Brozek, Susan Norris, et al. 2011. “GRADE guidelines: 1. Introduction.” Journal of Clinical Epidemiology 64 (4): 383–94. https://doi.org/10.1016/j.jclinepi.2010.04.026.
Holman, Bennett, and Justin P Bruner. 2017. “Experimentation by industrial selection.” Philosophy of Science 84 (5): 1008–19.
Morganroth, Joel, J Thomas Bigger, and Jeffrey L Anderson. 1989. “Treatment of ventricular arrhythmias by United States cardiologists: a survey before the Cardiac Arrhythmia Suppression Trial Results were available.” American Journal of Cardiology 65: 40–48.
OCEBM Levels of Evidence Working Group. 2011. “The Oxford 2011 Levels of Evidence.” Technical report. Oxford Centre for Evidence-Based Medicine. http://www.cebm.net/ocebm-levels-of-evidence/.
Sackett, David L, W. M C Rosenberg, J A Muir Gray, R B. Haynes, and W Scott Richardson. 1996. “Evidence based medicine: what it is and what it isn’t.” BMJ 312 (7023): 71–72. https://doi.org/10.1136/bmj.312.7023.71.
Stampfer, M J, and G A Colditz. 1991. “Estrogen replacement therapy and coronary heart disease: a quantitative assessment of the epidemiologic evidence.” Preventive Medicine 20 (1): 47–63.
Women’s Health Initiative Investigators. 2003. “Estrogen plus progestin and the risk of coronary heart disease.” New England Journal of Medicine 349 (6): 523–34. https://doi.org/10.1056/NEJMoa1404304.